Improving Patient Outcomes with SAVI and Google Cloud’s Innovative Surgical Instrument Tracking
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Powered by Vertex AI (Google Cloud’s platform for accelerating development and deployment of machine learning models into production), SAVI (Semi Automated Vision Inspection)1 is transforming surgical instrument identification and cataloging, leading to fewer canceled surgeries and easing pressure on surgery waitlists.
Max Kelsen, an analytics and software agency that specializes in machine learning, has worked closely with Google Cloud and Johnson & Johnson MedTech to create a system that can manage tens of thousands of individual devices, their characteristics, and how they apply to each set or tray used by a surgeon. SAVI does this while delivering a one in 10,000 real-world error rate, much faster and more accurately than manual processes currently in use across the industry. Implementing SAVI can also unlock end-to-end visibility and traceability across the surgical set supply chain and provide advanced analytics and insights.
Eliminating time-consuming manual processes
Surgeons need a large number of specialist instruments and devices to complete complex, delicate procedures. Because each tray of these instruments can typically cost more than $350,000, and having every type of set on shelf at every surgical facility is not feasible, manufacturers generally loan them to hospitals for procedures, such as inserting one of the manufacturers’ implants into a patient’s knee. Once a procedure is complete, the hospital returns the instrument tray to the manufacturer for storage and re-distribution to other hospitals as needed.
Each time a hospital returns a tray, the manufacturer needs to check that each instrument is there, correctly placed, cleaned, and fit for the purpose of the next procedure. As each set may hold more than 400 instruments, completing this process manually is complex and time-consuming. While each tray is checked before and after surgery at the hospital, and again when it arrives and leaves the manufacturer’s facility, Max Kelsen finds that 5% of surgeries can still be affected by missing, broken or bent instruments. This has a severe downstream impact on private hospitals in particular, directly affecting patient safety and outcomes; in Australia, for example, around 60% of surgeries are performed in private hospitals.
Johnson & Johnson MedTech has 60,000 surgical trays across the Asia-Pacific, and loans these trays out about 100,000 times per month. The manufacturer approached Max Kelsen to help design and develop a solution to make the supply chain more efficient, and to give more visibility into asset movement. As a Google Cloud Partner specializing in applying machine learning at scale in healthcare contexts, Max Kelsen had the expertise and track record to meet Johnson & Johnson MedTech’s need for a globally scalable solution that was engineered for quality and performance.
The first step was to establish a baseline for the project by determining how long the manufacturer’s team took to process each tray, and to set an efficiency number. We then spent six months determining and evaluating how to deliver a robust, accurate solution that outperformed current manual and labor-intensive methods in processing instruments and trays, globally. Our work included extensive technical feasibility research involving a representative sample for the variety and complexity of sets, trays, and devices needed for different types of surgery, including orthopedics, spinal trauma, and maxillofacial groups.
Working with Google Cloud to accelerate and de-risk the project
This is a familiar problem that is industry-wide. The issue has been widely explored and tried with a number of technologies over several years without producing the scalability and performance results required to make this an appropriate and feasible solution. Google Cloud partnered with Max Kelsen to accelerate and de-risk this large and strategic project for a mutual customer.
Technical feasibility took four months, prior to a year-long production pilot of SAVl in a distribution center in Queensland that services over 100 hospitals. After obtaining enough real-world data and experience to validate that the solution was as scalable and as accurate as needed, an Asia-Pacific rollout of the system commenced. SAVI is now live across Johnson & Johnson MedTech’s operations in Australia, New Zealand, and Japan, garnering recognition with a JAISA excellence award.
Google Cloud machine learning is integral to SAVI. Google Cloud’s technologies were a big differentiator for Max Kelsen’s engineering team in delivering the breakthroughs needed at scale, and in production, to meet Johnson & Johnson MedTech’s needs.
Reducing checking and documentation time
Running SAVI in Google Cloud has reduced the time Johnson & Johnson MedTech needs to check and document inspections of these surgical instrument sets by over 40%. The application also delivers consistent measurable quality that is often hard to measure at scale when using manual processes. During the pandemic, the application enabled Johnson & Johnson MedTech to operate with a lower headcount for the same volume output, enabling the organization to quickly service a backlog of waiting list surgeries.
In addition, the automation delivered with SAVI has reduced the time required to bring technicians up to speed on quality control processes, from eight to 12 months down to just three months, enhancing productivity and performance while delivering a more robust workforce.
So how does SAVI work in a real-world context? SAVI is deployed via a tablet and a web-based application incorporates an API to photograph the medical device trays, as shown below. Max Kelsen captures the photograph and sends it to a range of different services, via an API endpoint hosted on Google Cloud:
- Image information is stored in Cloud Storage
- Data relating to the trays is stored in Cloud SQL for PostgreSQL
- APIs and web UI components run in CloudRun
- Analytics data is stored within BigQuery
Once this tray and device onboarding stage is completed, the next step is to perform inferences from the images and data. By hosting online models with Kubeflow model serving on GKE, we enable a model to identify all the instruments in a tray at low latency.
Vertex AI Workbench notebooks are used for data exploration and modeling. Kubeflow training pipelines hosted on GKE are executed to produce machine learning models for specific surgical instrument sets. Several hundred machine learning models are then hosted with Kubeflow model serving on GKE, with state and analytics managed using Firebase. Using machine learning to infer from images whether any devices are incorrectly placed, dirty, or otherwise not fit for purpose, the data is then returned to the tablet for the user to respond accordingly.
Based on our success to date with SAVI, it is now available on Google Cloud Marketplace to help healthcare organizations achieve machine learning-powered efficiencies across a range of use cases, and ultimately improve patient safety and outcomes.
Not to be confused with the usage of Visual Inspection Model (Assembly) available in Vertex AI Vision
Headless e-Commerce is the Next Big Thing in Retail
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Headless Ecommerce
In the last couple of years there has been a shift in the way retailers approach ecommerce: where in the past development efforts were prioritized around building a solid foundation for backend transactions and operations now it is clear that companies in this space are focusing on differentiating themselves by creating unique shopping experiences that increase engagement and reduce friction.
But how can development teams spend the necessary time designing and writing code for this kind of interactions while also having to seamlessly maintain ecommerce vital components like online catalogs, shopping carts and checkout payment processes? Enter headless commerce.
Headless commerce (HC) helps companies of all sizes to innovate, develop and launch in less time and using fewer resources by decoupling backend and frontend. Headless solution providers empower online retailers by offering a balance between flexibility and optimization through pre-built api-accessible modules and components that can be easily plugged into their frontend architecture. This translates into rapid development while keeping desired levels of security, compliance, integration and responsiveness.
This composable approach enables dev teams not only to create new features but also connect other ecommerce components with less effort which is critical when responding to business trends. But above all, the main benefit retailers receive from HC, is owning and controlling the frontend for an engaging customer journey as well as quickly launching new experiences.
Google Cloud + commercetools
commercetools, a leader in the headless commerce space, has partnered with Google Cloud to make their cloud-native SaaS platform available in the Google Cloud Marketplace. With a flexible API system (REST API and GraphQL), commercetools’ architecture has been designed to meet the needs of demanding omnichannel ecommerce projects while offering real flexibility to modify or extend its features. It supports a variety of storefront providers like Vue Storefront, offers a large set of integrations and supports microservice-based architectures. All this while providing access to multiple programming languages (PHP, JS, Java) via its SDK tools.
commercetools and Google Cloud provide development teams with all the tools to build high-quality digital commerce systems. Google Cloud’s scalability, AI/ML components, API management capabilities and CI/CD tools are a perfect fit to build frontend shopping experiences that easily integrate with the commercetools stack. Developers can take advantage of this compatibility by:
- Integrating systems with Google’s Retail Search, Recommendations AI and Vision Product Search
- Injecting serverless functions into commercetools using Google Cloud Functions
- Extending and integrating commercetools via Events handled by Pub/Sub
- Managing 3rd party, legacy, microservices and commercetools APIs with Apigee
- Selecting the Google Cloud region commercetools uses for zero latency for custom apps
- Expanding their microservice ecosystem with components like Cloud Storage, Cloud SQL, Firestore and BigQuery
Additionally, commercetools allows ecommerce solutions to tap into the wider Google Ecosystem by providing authoritative data via Merchant Center, advertising via product listing ads and selling via Google Shopping.
Architecture Overview
As mentioned previously, headless commerce is increasingly preferred by retailers who want to own and control the ‘front-end’ for providing and enabling an engaging and differentiated user and shopping experiences.
The approach involves a loosely coupled architecture that separates ‘front end’ from the ‘back end’ of a digital commerce application. The front end is typically built and managed by the retailer. They want to leverage an independent software vendor (ISV) offered, ready-to-use ‘back-end’ commerce building blocks for capabilities, such as product catalog, pricing, promotions, cart, shipping, account and others.
Most retailers want to invest their time and resources in building a front end that requires an agile development model to introduce new and tweaking existing user experiences to acquire and retain customers. A few retailers that do not have an in-house web development team may choose an ISV that offers ready to use front end. The front end is a web app and designed as a progressive web application (PWA) on Google Cloud. The backend is a headless commerce offered by an ISV, such as commercetools. The backend commerce capabilities are built as a set of microservices, exposed as APIs, run cloud-native and implemented as headless. It is commonly referred to as the “MACH” solution. The API-first approach of the architecture allows easily integrating ‘best of breed’ capabilities built internally and/or offered by 3rd party ISVs.
Leveraging Google Cloud Components
The architecture of the front end will be implemented on Google Cloud and will integrate with the ISV’s headless commerce back end that runs natively in Google Cloud.
The front end will be designed using cloud-native services for
- PWA web app development (Google Kubernetes Engine, CI/CD services),
- Google Product Discovery solution that includes Retail Search and Vision API Product Search for serving product search (text and image) and Recommendations AI for serving recommendations.
- Storage (Cloud Storage), Database (Cloud SQL, Cloud Firestore), and edge caching for content delivery (Cloud CDN)
- Networking (Cloud DNS, Global Load Balancing), and
- Security (Cloud Armor for DDoS, API Defense for API protection)
Additionally, API management (Apigee on Google Cloud) can be used to orchestrate interactions of the front end with the APIs of the backend commerce services. The API management’s capability will be used for accessing the services of on-premises systems, such as ERP, order management system (OMS), warehouse management system (WMS) as needed to support the functioning of digital commerce application. Alternatively, depending on the frontend capabilities, developers can use middleware to build custom services and route requests.
What’s next?
A considerable number of retailers have adopted headless commerce and are now focusing on adopting best practices and leveraging the agility that comes with this approach. Just like commercetools offers robust components that meet the retailer’s backend operational needs (Product Catalog, Order Management, Carts, Payments, etc), Google Cloud’s Compute, Networking, Severless and AI/ML services provide the agility and flexibility required by development teams to quickly and easily extend their frontend capabilities.
commercetools and Google Cloud work seamlessly together because they both prioritize ease of integration, scalability, security and iterability while providing ready-to-use building blocks. It also helps that commercetools backend runs on Google Cloud. Once an initial foundation of Google Cloud and commercetools has been established, adding new commerce modules and extending functionally of the current ones becomes a straightforward process that allows to route efforts to innovation initiatives. In the end, the main beneficiaries of this technical synergy are the shoppers that enjoy experiences which increase engagement and minimize friction.
Alternatively, retailers can also save time and resources by relying on frontend integrations. commercetools offers a variety of third-party solutions that can effortlessly be added to a headless commerce architecture. These integrations as well as other important headless commerce extensions will be explored in future blog entries. In the meantime, all the necessary tools to leverage headless commerce can be found in just one place:
Get started with commercetools on the Google Cloud Marketplace today!
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Building Ethical AI: Why Organizations Need to Define Their Own Principles
In this video, learn about the importance of responsible AI, and how Google implements responsible AI in their products. You will also get an introduction to Google’s 7 AI principles.
Want to learn more about the importance of responsible AI? Enroll on Google Cloud Skills Boost → https://goo.gle/3CGhlXo
View the Generative AI Learning path playlist → https://goo.gle/LearnGenAI
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Dataplex: Google Cloud’s Intelligent Data Fabric to Manage Data and Analytics at Scale
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Enterprises are struggling to make high quality data easily discoverable and accessible for analytics, across multiple silos, to a growing number of people and tools within their organization. They are often forced to make tradeoffs—to move and duplicate data across silos to enable diverse analytics use cases or leave their data distributed but limit the agility of decisions.
Today we are excited to announce Dataplex, an intelligent data fabric that provides a way to centrally manage, monitor, and govern your data across data lakes, data warehouses and data marts, and make this data securely accessible to a variety of analytics and data science tools.
Dataplex provides an integrated analytics experience, bringing together the best of Google Cloud and open source tools, so you can rapidly curate, secure, integrate, and analyze data at scale. With built-in data intelligence using Google Artificial Intelligence (AI) and machine learning (ML) capabilities and a flexible consumption model, you can now spend less time wrestling with infrastructure and more time focused on driving business outcomes.
Dataplex enables you to:
- Achieve freedom of choice to store data wherever you want for the right price/performance and choose the best analytics tools for the job, including Google Cloud and open source analytics technologies such as Apache Spark and Presto.
- Enforce consistent controls across your data to ensure unified security and governance
- Take advantage of the built-in data intelligence using Google’s best in class AI/ML capabilities to automate much of the manual toil around data management and get access to higher quality data.
Early customers like Equifax, Loblaw, and ANZ are excited about using Dataplex to address data management complexity.
“Dataplex will greatly simplify the existing analytics workflows within Equifax with its unified data fabric and single interface for policy management and governance across all our analytics data. Its built-in data discovery and data quality features will ensure that our data scientists and analysts always have access to high quality data that they can trust. Dataplex aligns well with our enterprise data strategy and we are excited to partner with Google Cloud on this.”
—Kumar Menon, SVP, Data Fabric & Decision Science Technology, Equifax.
“Loblaw is Canada’s food and pharmacy leader, and we are excited to be an early adopter of Dataplex. We could significantly benefit from Dataplex as it provides a single pane of glass for end-to-end data management and governance. We are particularly interested in improving platform resilience and data quality by detecting anomalies as early as possible in the data pipeline with the help of Dataplex.”
—Elton Martins, Senior Director of Data Insights & Analytics, Loblaw
“We are undergoing a major data transformation at ANZ, bringing together our various data assets and building a cohesive data ecosystem for customer benefit. Dataplex’s vision and capabilities align well with our current data strategy to build a unified data fabric for all our analytics and AI/ML use cases. We are excited to partner with GCP on Dataplex and test the product in private preview.”
—Ashish Shekhar, Head of Technology – Enterprise Analytics & Applied AI, ANZ
Dataplex is built for distributed data. We are starting with data stored in Google Cloud Storage and BigQuery, with support for other data sources coming soon. It provides a workflow-driven experience helping you build an open data platform and make data easily accessible to your end users while ensuring your policies and best practices are consistently enforced.
Organizing and curating your data
One of the core tenets of Dataplex is letting you organize and manage your data in a way that makes sense for your business, without data movement or duplication. For that, we are providing logical constructs like lakes, data zones and assets. Those constructs enable you to abstract away the underlying storage systems and become the foundation for setting policies around data access, security, lifecycle management, and so on.
For example, you can create a lake per department within your organization (Retail, Sales, Finance, etc.) and create data zones that map to data readiness and usage (landing, raw, curated_data_analytics, curated_data_science, etc.).
Once you have your lakes and zones setup, you can now attach data to these zones as assets. You can add data from different types of storage (e.g. GCS Bucket and BigQuery dataset) under the same zone. You can also attach data across multiple projects under the same zone.
You can ingest data into your lakes and zones using the tools of your choice including services such as Dataflow, Data Fusion, Dataproc, Pub/Sub or choose from one of our partner products. Dataplex provides built-in 1-click templates for common data management tasks.
Securing your data
Dataplex enables you to define and enforce consistent policies across your data, irrespective of where it physically resides. Data owners can easily set up policies for specific data domains based on business needs, without thinking about where the data is stored while data stewards get global visibility into governance policies and permissions across their data.
You can apply security and governance policies for your entire lake, a specific zone, or an asset. Dataplex maps the policies to the underlying storage and pushes down permissions to the storage layer to provide end-to-end secure data access. Additionally, you can secure not just data but also related artifacts like notebooks, scripts, and models using the same set of access policies.
Making high quality data available for analytics and data science
One of the biggest differentiators for Dataplex is our data intelligence capabilities using Google’s best in class AI/ML technologies. As you bring the data under management, Dataplex will automatically harvest the metadata for both structured and unstructured data, with built-in data quality checks. All of the metadata is automatically registered in a unified metastore, and made available for search and discovery. It is also published to Bigquery, Dataproc Metastore, and Data Catalog – ensuring that you have the same consistent data context and access across your tools.
For example, when you write parquet files to a Google Cloud Storage bucket – Dataplex will automatically extract metadata of these files, detect a tabular schema, including hive-style partitions, run data quality checks, and make this data queryable in BigQuery as an external table and from any open source or partner application – with the same consistent security and access policies you defined at the logical data layer.
Your data scientists and analysts now have secured access to this data that meets your quality bar and governance rules via the tools of their choice, without needing any additional processing.
One-click access to collaborative analytics
Dataplex provides fully managed, one-click analytics environments enabling you to use the power of Apache Spark and BigQuery with support for other engines coming in the future.
As data administrators, you now have the flexibility to pre-configure these environments with the right cost and financial governance measures without taking on the overhead of managing and maintaining the infrastructure required to power these environments. You can easily configure different environments for different types of workloads and share it with multiple users using their IAM credentials. Dataplex manages the provisioning, monitoring, scaling, and shutdown of these environments.
As data scientists, analysts, and engineers, you now have a turn-key experience to run your analysis using notebooks and a SQL workbench. You can search for notebooks and scripts alongside data, save and share your work with other users, and schedule your notebooks or scripts for recurring workloads – all using the same integrated experience within Dataplex.
Building an open platform with industry leaders
We are partnering with industry leaders such as Accenture, Collibra, Confluent, Informatica, HCL, Starburst, NVIDIA, Trifacta, and others to build an open platform to power analytics at scale. Our partners are excited about the capabilities that Dataplex will provide:
“Collibra is excited to partner with Dataplex to provide data governance and data quality for consistent controls across distributed data. Pairing Collibra’s multi-cloud and hybrid solution with Dataplex allows enterprises to securely open up access to more, higher quality data for users and analytics using a single unified view.”
—Jim Cushman, Chief Product Officer, Collibra
“Dataplex builds on Google Cloud’s commitment to open source by integrating with Apache Kafka®, a leading open source platform for event streaming. We at Confluent, the platform for data in motion that completes Apache Kafka® to be enterprise-ready, are excited to partner with Dataplex to enable customers to bring together distributed, real-time data and build a unified data fabric for end to end analytics.”
—Paul Mac Farland, VP, Head of Customer Solutions and Innovation, Confluent
“We are excited to partner with Google Cloud’s Dataplex team as we look to provide our joint customers an integrated and open data fabric for analytics at scale. Extending Dataplex’s data management and data quality capabilities with Starburst Enterprise will accelerate time to value for enterprises looking to connect distributed data without having to move data.”
—Justin Borgman, CEO and Co-founder, Starburst
Next Steps
Dataplex is now available in preview for a select number of customers. For more information, visit our website or watch the recording. If you would like to sign up, please click here.
How the Telegraph is Reimagining Media with Google Cloud
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Whether they’re reading the newspaper on the way to work, or catching up on the latest headlines on their smartphones, readers expect up-to-the-minute news wherever and whenever makes the most sense for them. As a result, media companies are increasingly looking for ways to improve, expand, and simplify their offerings, and they’re increasingly looking to the cloud to do it.
For more than 160 years The Telegraph has been counted on by readers across the United Kingdom and globally for award-winning news and journalism. An early adopter of cloud technology, it’s been a G Suite customer since 2008 and has already been using Google Cloud Platform to analyze digital behaviors to improve engagement and advertising performance since 2016.
Recently, The Telegraph announced it’s migrating fully to Google Cloud. By migrating all their production and pre-production services, they aim to deliver content faster, provide compelling experiences to readers, and reduce environmental impact.
“We are delighted to announce our newest collaboration with Google Cloud,” said Chris Taylor, Chief Information Officer, The Telegraph. “We have always worked closely with Google as they help us to provide our readers with great experiences on our digital products, collaboration software and internet scale through search. Their continued leadership in projects such as Kubernetes are enabling us to build flexible development environments that truly support DevOps.”
Powering the Digital Publishing Ecosystem
The Telegraph produces large volumes of digital content every day. It was imperative for them to find a cloud provider they could trust to support this ecosystem. By working with Google Cloud they have changed the way they see and engage with data: they can collect new information about their products every second and use that to continually hone their strategy. The Telegraph are placing more confidence and trust in the data captured about their content and now have one of the best available pieces of technology for capturing and analyzing the stories they publish in real-time.
Leveraging AI to support journalists
Time is critical when journalists are on a story, and The Telegraph wants to put important data in the hands of its journalists right when they need it. To do this, it will be using AutoML to classify content for journalists and make it more discoverable. For example, a reporter will be able to bring up relevant assets that link to their stories. It will also apply AutoML to classify Telegraph stock photos to help journalists attach compelling visual content to their stories faster.
Building compelling reader experiences with the help of APIs
Readers have an ever-increasing expectation of personalization. To meet this need, The Telegraph launched My Telegraph, currently live in beta, to offer registered readers personalized news experiences based on their interests or the particular journalists they want to follow. My Telegraph was developed on an API management platform provided by Google Cloud’s Apigee. You can learn more about how it’s applying API management to My Telegraph, in this blog post.
Working for environmental good
The Telegraph is the biggest selling quality newspaper in the UK, an accolade which requires it to print and distribute hundreds of thousands of copies each day. Optimal management of print production is important, and by using a combination of the cloud and machine learning, The Telegraph is better able to predict demand for physical newspapers, maximizing sales and minimizing waste. This makes great business sense for The Telegraph but also has great environmental benefit.
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Breakthroughs in artificial intelligence (AI) have captured the imaginations of business and technical leaders alike. The AI techniques underlying these breakthroughs are finding diverse application across every industry. Early adopters are seeing results, particularly encouraging is that AI is starting to transform processes in established industries, from retail to financial services to manufacturing.
However, an organization’s effectiveness in applying these breakthroughs is anchored in the basics: A disciplined foundation in capturing, preparing and analyzing data.
Data scientists spend up to 80% of their time on the “data wrangling,” “data munging” and “data janitor” work required well before the predictive capabilities promised by AI can be realized.
Capturing, preparing and analyzing data creates the foundation for successful AI initiatives. To help business and IT leaders create this virtuous cycle, Google Cloud has prepared a CIO’s guide to data analytics and machine learning that outlines key enabling technologies at each step. Crucially, the guide illustrates how managed cloud services greatly simplify the journey—regardless of an organization’s maturity in handling big data.
This is important because, for many companies, the more fundamental levels of data management present a larger challenge than new capabilities like AI. “Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies,” noted Oliver Wyman consultants Nick Harrison and Deborah O’Neill in a recent Harvard Business Review article (aptly titled If Your Company Isn’t Good at Analytics, It’s Not Ready for AI). “Like it or not, you can’t afford to skip the basics.
Building on new research and Google Cloud’s own contributions to big data since the beginning, this guide walks readers through each step in the data management cycle, illustrating what’s possible alongside examples.
Specifically, the CIO’s guide to data analytics and machine learning is designed to help business and IT leaders address some of the essential questions companies face in modernizing data strategy:
- For my most important business processes, how can I capture raw data to ensure a proper foundation for future business questions? How can I do this cost-effectively?
- What about unstructured data outside of my operational/transactional databases: raw files, documents, images, system logs, chat and support transcripts, social media?
- How can I tap the same base of raw data I’ve collected to quickly get answers as new business questions arise?
- Rather than processing historical data in batch, what about processes where I need a real-time view of the business? How can I easily handle data streaming in real time?
- How can I unify the scattered silos of data across my organization to provide a current, end-to-end view? What about data stored off-premises in the multiple cloud and SaaS providers I work with?
- How can I disseminate this capability across my organization—especially to business users, not just developers and data scientists?
Because managed cloud services deal with an organization’s sensitive data, security is a top consideration at each step of the data management cycle. From data ingestion into the cloud, followed by storage, preparation and ongoing analysis as additional data flows in, techniques like data encryption and the ability to connect your network directly to the Google Cloud must reflect data security best practices that keep data assets safe as they yield insights.
Wherever your company is on its path to data maturity, Google Cloud is here to help. We welcome the opportunity to learn more about your challenges and how we can help you unlock the transformational potential of data.
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